Introduction to Vertex AI Workbench (original) (raw)
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Vertex AI Workbench is a Jupyter notebook-based development environment for the entire data science workflow. You can interact with Vertex AI and other Google Cloud services from within a Vertex AI Workbench instance's Jupyter notebook.
Vertex AI Workbench integrations and features can make it easier to access your data, process data faster, schedule notebook runs, and more.
For example, Vertex AI Workbench lets you:
- Access and explore your data from within a Jupyter notebook by using BigQuery and Cloud Storage integrations.
- Automate recurring updates to your model by using scheduled executions of your notebook's code that run on Vertex AI.
- Process data quickly by running a notebook on a Dataproc cluster.
- Run a notebook as a step in a pipeline by using Vertex AI Pipelines.
Instance types
Vertex AI Workbench offers several instance types:
- Vertex AI Workbench instances: An option that combines the workflow-oriented integrations of a managed notebooks instance with the customizability of a user-managed notebooks instance. To learn more, see Introduction to Vertex AI Workbench instances.
- Vertex AI Workbench managed notebooks(deprecated): A Google-managed option with built-in integrations that help you to set up an end-to-end notebook-based production environment. To learn more, see Introduction to managed notebooks.
- Vertex AI Workbench user-managed notebooks(deprecated): An option for users who need heavy customization and control over their environment. To learn more, see Introduction to user-managed notebooks.
All instance types are prepackaged withJupyterLaband have a preinstalled suite of deep learning packages, including support for the TensorFlow and PyTorch frameworks. You can use CPU-only or GPU-enabled instances. All instance types also integrate withGitHub so that you can sync your notebook with a GitHub repository.
All Vertex AI Workbench instance types are protected by Google Cloud authentication and authorization.